I am currently studying for a Bachelor in Mathematics from Imperial College London, from which I am graduating in June 2024. After graduation, I will continue my studies with the Advanced Computer Science master's at the University of Oxford, where I intend to focus on the mathematical foundations of machine learning. I have a strong desire to conduct research in this field and am actively seeking opportunities to do this.

Currently, I am interested in understanding the inner workings of neural networks. More specifically, I am intrigued by geometrically inspired approaches to this problem.

- Singular learning theory enhances classical statistical learning theory with algebraic geometry to develop a framework suitable for investigating neural networks.
- Approximating neural networks with spline functions has facilitated a geometric analysis of the inner workings of neural networks.
- Algebraic topology has been utilised for data analysis, however, its application to investigating neural networks is underdeveloped.
- The space of neural networks can be embedded into a manifold using the tools of information geometry.

Moreover, I am interested in frameworks that structure the development of machine learning architectures and thus facilitate the effective application of machine learning technology.

- Geometric Deep Learning.
- Categorical Deep Learning.

From my exploration of these ideas, I have also developed a broad interest in psychology, morality and theory of mind.